Contemporary Analysis Job Board: Data Scientist

Contemporary Analysis (CAN) is a global data science company based in Omaha, NE that provide predictive analytics to multiple Fortune 500 companies and small businesses in the United States, Europe and Asia.  CAN is focused on making analytics accessible to companies of all sizes and industries, and offers standard products and professional services.
The purpose of this position to help expand our professional services team.  CAN’s professional services team is responsible for developing solutions for CAN’s largest and most unique clients including Fortune 500 and Global Fortune 50 companies.  The by-products from the team’s professional services are used to create new and enhance existing CAN products.
Each Data Scientist is responsible for working with a CAN Sales Executive to understand each client’s business, define projects to help clients achieve their business objectives, use data science to develop solutions, and present results as a written report and presentation.  Data Scientists must be familiar enough with statistics and computer science to develop creative solutions, and have the written and verbal skills to develop compelling reports and presentations.
The Data Scientist will be responsible for:

1. Working with the Sales Executive, the Data Scientist will work at all executive levels to help design solutions that will meet the needs of the client.  To be able to design creative solutions that go beyond simple client feature requests will require Data Scientists to have an advanced familiarity with modeling, mathematics and statistics.  Also, during the discovery phase the Data Scientist will coordinate with the COO and Sales Executive to develop project budgets.
2. During the implementation phase, the Data Scientist will work with other CAN Data Scientists and vendors to implement the Analytical Blueprint, and monitor client results, and adjust the Analytical Blueprint to optimize the client results and experience.  Since CAN offers data science solutions as a service, implementation can last from a month to several years.  This creates a unique project management scenario that requires continuous monitoring to ensure that the project does not fall behind.
3. The Data Scientist working with the Sales Executive will maintain a positive relationship with the client, ensure ongoing deliverables are met, and assess any future need for CAN’s services.  In some cases the Data Scientist will need to record best practices from the project, or write specific business issue case studies.

Qualifications:

1. Minimum Education:  Bachelor’s Degree from an accredited institution.
2. Able to maintain focus in highly-charged environments and manage competing priorities.  This includes experience managing multiple projects simultaneously against tight deadlines
3. Experience solving business issues with the consultative application of advanced analytics and/or information technology
4. Strong presentation and client management skills – up to the highest executive level.  This includes being able to explain highly detailed and technical subject matter to non-technical audience, and being able to present and sell analytical concepts to clients
5. Experience delivering insight to internal or external clients by building on a technical foundation that includes a conceptual understanding of modeling techniques and a basic grasp of statistics.   Ability to use analytical applications to solve a practical problem, in an on the spot high-pressure situation
6. Experience in project management and managing a team to meet a deadline, manage client expectations, and maximize client satisfaction relative to solution profitability
7. Functional experience in one or more of the following areas, selling analytic services, project management, data science product development, pre-sales, technology implementation, and/or account management
8. Technical foundation including one or more of the following areas, Bayesian statistics, multiple regression analysis, and/or econometric modeling is preferred but not required.